New IBM Software Helps Analyze Data for Health Care Transformation
AUSTIN, Texas - (October 25, 2011) - Seton Healthcare Family is the first client to adopt IBM software that uses content analytics in an effort to improve patient care and reduce preventable hospital readmissions.
Seton will use the technology, called IBM Content and Predictive Analytics for Healthcare, to extract relevant clinical information from vast amounts of patient data to better analyze the past, understand the present, and predict future outcomes.
By combining IBM's Watson technology with industry solutions offerings, Seton intends to focus the new content and predictive analytics solution on the root causes of hospital readmissions, and ways it can decrease preventable hospital visits.
According to the New England Journal of Medicine, one in five patients incur preventable readmissions, which represents $17.4 billion of the current $102.6 billion Medicare budget.* Beginning in 2012, hospitals will be penalized for high readmission rates with reductions in Medicare discharge payments.
"IBM Content and Predictive Analytics for Healthcare uses the same type of natural language processing as IBM Watson, enabling us to leverage our unstructured information in new ways not possible before," said Charles J. Barnett, FACHE, president/chief executive officer, Seton Healthcare Family. "With this solution, we can access an integrated view of relevant clinical and operational information to drive more informed decision making. For example, by predicting readmission candidates, we can plan more appropriate interventions, thus reducing costly and preventable readmissions and ultimately improving the quality of life for our patients."
Most health care organizations are drowning in data but are challenged to gain reliable, actionable insights from this information. In fact, more than 80 percent of an institution's data today is unstructured. In health care, this is in the form of physician notes, registration forms, discharge summaries and other documents. Different from machine- ready data, this content lacks structure and is arduous for health care enterprises to analyze. As a result, millions of patient notes and records often sit unavailable in separate clinical data silos.
Seton will initially apply the solution to congestive heart failure (CHF) diagnoses. CHF readmission rates per patient are nine times greater than that for patients with other conditions, and their inpatient stays are 12 times longer on a per-patient basis. That is because CHF patients routinely struggle with multiple chronic illnesses, leading to increased readmissions and frequent doctor visits that can reduce their quality of life.
"We want to see if the software can help us improve proactive management of CHF patients and possibly flag new risk factors specific to our patient population," said David Ramirez, MD, medical director of Ambulatory Care at University Medical Center Brackenridge.
"If we can better understand the relationships buried in large volumes of clinical and operational data, it will help us redesign the delivery of care and services around patients' needs. That will make care safer, more efficient and effective, especially for patients at high risk for complications."
For more information IBM Content and Predictive Analytics for Healthcare, visit http://www.ibm.com/software/ecm/content-analytics/predictive/healthcare.html
For more information on IBM Watson, visit www.ibmwatson.com